Association of plasma BDNF and MMP-9 levels with mild cognitive impairment: a matched case-control study
Bibliographic record
Abstract
The prevalence of Alzheimer's disease (AD) is on the rise globally, and everyone who develops AD eventually experiences mild cognitive impairment (MCI) first. Timely intervention at an early stage of the disease may mitigate disease progression. Recent studies indicate that BDNF and MMP-9 play a significant role in the pathogenesis of AD. Therefore, this study aims to ascertain whether there are differences in plasma BDNF and MMP-9 levels between individuals with mild cognitive impairment due to AD and those with normal cognition, and to analyze the factors influencing mild cognitive impairment.This case-control study included 102 individuals with mild cognitive impairment and 102 controls, matched by age and sex. Participants completed a series of questionnaires, neuropsychological assessments, and clinical examinations. Plasma concentrations of BDNF and MMP-9 of the participants were quantified using ELISA. Subsequently, the factors influencing MCI were analyzed using univariate and multivariate logistic regression. The differences in plasma BDNF levels, MOCA total scores, and scores in various cognitive domains (including visuospatial and executive abilities, abstract thinking, attention, language, naming, and delayed memory) between the MCI and the control groups showed statistically significant (p < 0.05). Logistic regression analysis revealed that plasma BDNF levels and years of formal education were significantly negatively associated with MCI. This study indicates that plasma BDNF and years of formal education are protective factors influencing cognitive function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".